The Game of Defense and Security

This chapter covers the emerging area of the use of commercial off-the-shelf (COTS) computer games for military, defense and security purposes. A brief background is provided of the historic link between games and military simulation, together with the size and scope of the modern computer game industry. Considerable effort is dedicated to providing a representative sample of the various defense and security usages of COTS games. Examples of current usage are drawn from a range of nations including the United States (U.S.), Australia, Denmark, Singapore and Canada. Coverage is broken into the three chief application areas of training, experimentation and decision-support, with mention of other areas such as recruitment and education. The chapter highlights the benefits and risks of the use of COTS games for defense and security purposes, including cost, acceptance, immersion, fidelity, multi-player, accessibility and rapid technological advance. The chapter concludes with a discussion of challenges and key enablers to be achieved if COTS games are to obtain their true potential as tools for defense and security training, experimentation and decision-support. Aspects highlighted include the dichotomy between games for entertainment and “serious” applications; verification, validation and accreditation; collaboration between the games industry and defense; modifiability, interoperability; quantifying training transfer; and a range of technological challenges for the games themselves.

The Advancement of Positioning Technologies in Defense Intelligence

The adoption of positioning technologies to supplement, complement and function as defense intelligence applications has become widely accepted within homeland security and military circles. At the core of advancement are four main positioning technologies: the global positioning system (GPS), second generation (2G) and beyond mobile telephone networks (including wireless data networks), radio-frequency identification (RFID) and geographic information systems (GIS). For all positioning technologies, both separately and when combined, it is of primary importance to their continued adoption that the controlling powers have an in-depth understanding of the causality between implementation, usage and flow-on effect. This relies on an alignment of defense strategy, knowledge systems, security requirements and citizen rights within the broader social context. This social context must respond to continuing security breaches, advancements in technology and the ever-changing face of bureaucracy. There is, however, great difficulty in creating an uncompromising foundation for homeland security, which is at all times both void of complexity and suitable to all. Even more difficult, though, is to predict both the events and consequences that will herald from the systems now being created.

How Hard Is It To Red Team?

Red teaming is the process of studying a problem by anticipating adversary behaviors. When done in simulations, the behavior space is divided into two groups: one controlled by the red team, which represents the set of adversary behaviors or bad guys; the other controlled by the blue team, which represents the set of defenders or good guys. Through red teaming, analysts can learn about the future by forward prediction of scenarios. More recently, defense has been looking at evolutionary computation methods in red teaming. The fitness function in these systems is highly stochastic, where a single configuration can result in multiple outcomes. Operational, tactical and strategic decisions can be made based on the findings of the evolutionary method in use. Therefore, there is an urgent need to understand the nature of these problems and the role of the stochastic fitness to gain insight into the possible performance of different methods. This chapter presents a first attempt at characterizing the search space difficulties in red teaming to shed light on the expected performance of the evolutionary method in stochastic environments.

Simulating Complexity-Based Ethics for Crucial Decision Making in Counter Terrorism

“Counter-terrorism refers to the practices, tactics and strategies that governments, militaries and other groups adopt in order to fight terrorism.” Counter Terrorism (CT) is a complex system driven by political, stress and time pressures that contribute to the enormous difficulty that involved people face in making sustainable ethical decisions. This chapter proposes a systems planning approach for enhancing the sustainability of crucial ethical decisions in CT. First, we describe the need for enhancing crucial ethical decision-making using some recent cases. Next, we evaluate the relevance and utility of a systems planning approach in providing such enhancements for CT. We develop the “ideal state” for tools and techniques to be used for crucial ethical decision-making in CT. We propose the POWER systems planning framework as a model for advancing towards this ideal state Finally, we consider how games and simulation could be used to envision and inform, aid synthesis of and support evaluation of decision-making through the POWER model.

All Hazards Analysis

With the increase in the complexity of terrorism’s networks and activities, the advances in chemical and biological warfare, and the use of organized criminal activities, it is becoming apparent that dealing with this complexity is not possible with traditional problem-solving approaches. The artificial complexity area (Artificial Life, or ALife), complex systems and agent-based distillation (ABD) provide a new perspective to the problem and emphasize the importance of modeling the interaction between system components to tackle these issues. This chapter presents an introduction to Cellular Automota and ABD, and then reviews and critiques how these approaches specifically have been used to model aspects of bushfires, epidemics, biological warfare and terrorism. This chapter then extends upon previous works to present an overview of the possible use of artificial complexity models to the larger field of security and safety applications.

Distributed Intrusion Detection Systems

Computer security is defined as the protection of computing systems against threats to confidentiality, integrity and availability. An intrusion is defined as any set of actions that attempt to compromise the integrity, confidentiality or availability of a resource. The process of monitoring the events occurring in a computer system or network and analyzing them for sign of intrusions is known as Intrusion Detection System (IDS). A Distributed IDS (DIDS) consists of several IDS over a large network (s), all of which communicate with each other, or with a central server that facilitates advanced network monitoring. In a distributed environment, DIDS are implemented using co-operative intelligent agents distributed across the network(s). This chapter presents a framework for a DIDS comprised of a multi-agent framework with computational intelligent techniques, to reduce the data features to create lightweight detection systems and a hybrid-intelligent system approach to improve detection accuracy.

Small & Simple

This chapter discusses the use of abstract multi-agent models of conflict — ABDs (Agent-Based Distillation) — for security and defense purposes. The chapter promotes a complex-systems, bottom-up approach to modeling conflict in a highly abstracted manner. Three ABDs drawn from three application areas – Operations Analysis, Wargaming (decision support) and Counter-Terrorist Activity — are used as illustrations. The history, philosophy and design approach of ABDs are discussed, including the key features of abstraction, bottom-up modeling, ease of use and rapid exploration of the parameter space of a problem. Challenges facing the successful design, implementation and deployment of ABDs are discussed, including appropriate level of abstraction, validation, acceptance, parameter setting and understanding of the range of outcomes generated. The Conceptual Research Oriented Combat Agent Distillation Implemented in the Littoral Environment (CROCADILE) ABD is used to illustrate the design of an ABD, highlighting that they are not complex pieces ofsoftware by military simulation standards. A patrol scenario is constructed in which a team of patrolling agents seeks to intercept an infiltrator crossing a border. The relative benefits of increasing sensor and communication capabilities of the patrol are explored in terms of the patrol’s ability to successfully intercept the infiltrator. The Tactical Decision Support System (TDSS) ABD shows a different application of the technology — in this case, wargaming Courses Of Action (COA). An experiment is described utilizing officers from the Australian Royal Military College to wargame COA using both traditional and TDSS approaches. For the TDSS approach, significant improvement in outcomes across a range of criteria was found by both the officers using the system and the evaluating staff. Finally, the System for Life and Intelligence Modeling (SLIM) ABD is used to explore the consequences of a government’s arrest policies in a homeland defense scenario. The model incorporates different geographic regions (homeland, foreign deployment, etc.), borders, terrorists, civilians, police, military, forensic evidence and bomb attacks. The consequences of lowering or raising the arrest threshold in terms of the amount of evidence required against an individual agent are shown in terms of bomb attacks, deaths, terrorists arrested and civilians wrongly arrested.

Network Robustness for Critical Infrastructure Networks

Events of the past few years have shown how today’s modern technological society is critically dependent on critical infrastructure networks such as telecommunications, transport and power. In this chapter, we examine the robustness of critical infrastructure networks and describe some simulation studies exploring this issue. These studies use an extension of data farming we call “network farming,” implemented within the CAVALIER network analysis tool suite. We then survey some historical data on actual terrorist attacks and show that the distribution of these attacks in time can be modeled by a Poisson statistical distribution. This fact can then be used to plan robust network architectures. We alsoexamine “scale-free networks,” and show how they relate to the robustness of physical and organizational networks. In particular, we study the implications for law-enforcement personnel responding to terrorist organizations, using two historical case studies. Finally, we briefly survey emerging trends in network modeling and intelligent software agents that may influence the robustness of future networks.

Realized Applications of Positioning Technologies in Defense Intelligence

Spurred by the recent escalation of terrorist attacks and their increasingly devastating outcomes, defense intelligence in the context of homeland security has been drawn into the spotlight. The challenge, at both national and global levels, of managing information in order to offensively resist attack or defensively keep citizens safe from further harm has never been greater. In meeting this challenge, the tools and strategies used by relevant defensive powers are a key factor in the success or failure of all activities, ranging from small-scale, homeland security administration through to large-scale, all-inclusive war. In all areas within this wide scope, the adoption of positioning technologies has played an important role. Of special significance are the global positioning system (GPS), second-generation (2G) and beyond mobile telephone networks (includingwireless data networks), radio-frequency identification (RFID) and geographic information systems (GIS). Since most positioning technology has been developed or released for use within the commercial sector, however, concerns beyond technological feasibility are raised when applications are harnessed solely for defense. The integration between commercial and military sectors and public and private needs must be considered and, primarily, this involves ensuring that the enforcement of homeland security does not compromise citizen rights.